Semantic Retrieval Augmentation Effects on LLM Pass@k in Multi-File Code Generation

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does semantic retrieval augmentation impact pass@k scores for LLMs on multi-file code generation benchmarks compared to standard context window extension. Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does semantic retrieval augmentation impact pass@k scores for LLMs on multi-file code generation benchmarks compared to standard context window extension?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.